Available functions

library(pmplots)
library(dplyr)
library(mrggsave)
library(purrr)

Example data in the package

df <- pmplots_data_obs() %>% mutate(CWRES = CWRESI)

id <- pmplots_data_id()

dayx <- defx(breaks = seq(0,168,24))

.yname <- "NoDoz (ng/mL)"

etas <- c("ETA1//ETA-CL", "ETA2//ETA-V2", "ETA3//ETA-KA")

covs <- c("WT//Weight (kg)", "ALB//Albumin (g/dL)", "SCR//Creatinine (mg/dL)")

Override the df and id objects in the above chunk

## Nothing here

col//title specification

This is a way to specify the column name for source data along with the axis label

col_label("CL//Clearance (L)")
. [1] "CL"            "Clearance (L)"

When only the column is given, then the column name will be used for the column title:

col_label("WT")
. [1] "WT" "WT"

Observed vs predicted

Observed versus population predicted (dv_pred)

dv_pred(df, yname = .yname)

Observed versus population predicted - log/log

dv_pred(df, loglog=TRUE, yname = .yname)

Observed versus individual predicted (dv_ipred)

dv_ipred(df, yname=.yname)

Observed versus individual predicted - log/log

dv_ipred(df, loglog=TRUE, yname = .yname)

Observed versus both PRED and IPRED

dv_preds(df) %>% mrggdraw(ncol = 2)

Residual plots

Residuals

Residuals versus time (res_time)

res_time(df)

Residuals versus time after first dose (res_tafd)

res_tafd(df)

Residuals versus time after dose (res_tad)

res_tad(df)

Residuals versus population predicted (res_pred)

res_pred(df)

RES versus continuous covariate (res_cont)

res_cont(df, x="WT//Weight (kg)")

This function is also vectorized in x.

c("WT", "CRCL", "AST") %>% map(.f = partial(res_cont,df)) %>% mrggpage %>% mrggdraw

RES by categorical covariate (res_cat)

dplyr::count(df, STUDYc)
. # A tibble: 4 x 2
.   STUDYc      n
.   <fct>   <int>
. 1 SAD       424
. 2 MAD      1199
. 3 Renal     960
. 4 Hepatic   559
res_cat(df, x="STUDYc//Study type")

Residual histogram (res_hist)

res_hist(df)

Weighted residuals

Weighted residuals versus time (wres_time)

wres_time(df)

Weighted residuals versus time after first dose (wres_tafd)

wres_tafd(df)

Weighted residuals versus time after dose (wres_tad)

wres_tad(df)

Weighted esiduals versus population predicted (wres_pred)

wres_pred(df)

WRES versus continuous covariate (wres_cont)

This function is also vectorized in x.

wres_cont(df, x="WT//Weight (kg)")

WRES by categorical covariate (wres_cat)

wres_cat(df, x="STUDYc//Study type")

Weighted residual histogram (wres_hist)

wres_hist(df)

WRES QQ plot (wres_q)

wres_q(df)

Conditional weighted residuals (CWRES)

CWRES versus time (cwres_time)

cwres_time(df)

Conditional weighted residuals versus time after first dose (cwres_tafd)

cwres_tafd(df)

CWRES versus time after dose (cwres_tad)

cwres_tad(df)

CWRES versus continuous covariate (cwres_cont)

cwres_cont(df, x="WT//Weight (kg)")

Vectorized version

cwres_cont(df, covs) %>% 
  mrggdraw(ncol = 2)

CWRES by categorical covariate (cwres_cat)

cwres_cat(df, x="STUDYc//Study type")

cwres_cat(df, x="STUDYc//Study type", shown=FALSE)

Vectorized version

cwres_cat(df, x = c("STUDYc//Study", "RF//Renal Function"))
. [[1]]

. 
. [[2]]

Conditional weighted residual histogram (cwres_hist)

cwres_hist(df)

CWRES versus population predicted (cwres_pred)

cwres_pred(df)

CWRES QQ plot (cwres_q)

cwres_q(df)

NPDE plots

NPDE versus TIME (npde_time, npde_tad, npde_tafd)

npde_time(df)

NPDE versus TAD (npde_tad)

npde_tad(df)

NPDE versus TAFD (npde_tafd)

npde_tafd(df)

NPDE versus PRED (npde_pred)

npde_pred(df)

NPDE versus continuous variable (npde_cont)

npde_cont(df, "WT")

NPDE versus categorical variable (npde_cat)

npde_cat(df, "STUDYc")

QQ-plot with NPDE (npde_q)

npde_q(df)

NPDE histogram (npde_hist)

npde_hist(df)

ETA plots

etas <- c("ETA1//ETA-CL", "ETA2//ETA-V2", "ETA3//ETA-KA")
covs <- c("WT//Weight (kg)", "ALB//Albumin (g/dL)", "SCR//Creatinine (mg/dL)")

ETA versus continuous covariates (eta_cont)

Grouped by eta

eta_cont(id, x=covs,y=etas[2]) %>% pm_grid()

Grouped by covariate

eta_cont(id, x=covs[1], y=etas) %>%
  mrggdraw(ncol = 2)

ETA by categorical covariates (eta_cat)

p <- eta_cat(id, x="STUDYc//Study type", y=etas)
pm_grid(p)

ETA histograms (eta_hist)

etas <- c("ETA1//ETA-CL", "ETA2//ETA-V2", "ETA3//ETA-KA")
p <- eta_hist(id,etas, bins=10)
pm_grid(p)

ETA pairs plot (eta_pairs)

p <- eta_pairs(id,etas)
print(p)

DV versus time (dv_time)

Basic plot

dv_time(df, yname = .yname)

Faceted

dv_time(df, yname="NoDoze (ng/mL)") +
  facet_wrap(~DOSE, scales="free_x")

log-Scale

dv_time(df, yname="NoDoze (ng/mL)", log=TRUE) +
  facet_wrap(~STUDYc)

Data summary

Continuous variable by categorical variable (cont_cat)

cont_cat(id, x="STUDYc", y="WT")

General histogram (cont_hist)

cont_hist(id, x = "WT", bins = 20)

Split and plot (split_plot)

p <- split_plot(df, sp="STUDYc", fun=dv_ipred)
pm_grid(p)

Some customization

Modify x-axis

a <- list(trans="log", breaks = logbr3())

dv_time(df, xs=a)

Modify y-axis

dv_time(df, ys=a, yname="Y-axis name")

Drop extra layers

dv_pred(df, smooth=NULL)

dv_pred(df, abline=NULL)

dv_pred(df, abline=NULL, smooth = NULL)

cwres_time(df, hline = NULL)

Drop all extra layers

dv_pred(df, add_layers=FALSE)

Custom breaks

Default breaks:

dv_time(df)

Break every 3 days

dv_time(df, xby=72)

Custom breaks and limits

a <- list(br = seq(0,240,48), limits=c(0,240))
dv_time(df, xs=a)

Extra reference lines to [C]WRES plots

wres_time(df) + geom_3s()

Replicate look and feel

p <- ggplot(df, aes(IPRED,DV)) + geom_point()

p

Theme

p + pm_theme()

Plain

p + theme_plain()

Smooth

p + pm_smooth()

Abline

p + pm_abline()

Horizontal reference line

ggplot(df, aes(TIME,CWRES)) + geom_point() + pm_hline()

Rotate x and y axis labels

dv_pred(df) + rot_x(angle = 90) + rot_y()